Description
Scitus Academics Robust Speech Recognition And Understanding-Scitus by Danel Jaso
Speech recognition systems have become much more robust in recent years with respect to both speaker variability and acoustical variability. Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. In addition to achieving speaker independence, many current systems can also automatically compensate for modest amounts of acoustical degradation caused by the effects of unknown noise and unknown linear filtering. As speech recognition and spoken language technologies are being transferred to real applications, the need for greater robustness in recognition technology is becoming increasingly apparent. Substantial progress has also been made over the last decade in the dynamic adaptation of speech recognition systems to new speakers, with techniques that modify or warp the systems' phonetic representations to reflect the acoustical characteristics of individual speakers. Speech recognition systems have also become more robust in recent years, particularly with regard to slowly-varying acoustical sources of degradation. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies.
Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. Additionally, it presents a comprehensive survey of the state-ofthe-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences.